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A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands

Author

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  • Vahid Gholami

    (Department of Range and Watershed Management and Department of Water Engineering and Environment, Faculty of Natural Resources, University of Guilan, Rasht, Iran)

  • Mohammad Reza Khaleghi

    (Torbat-e-Jam Branch, Islamic Azad University, Torbat-e-Jam, Iran)

Abstract

Simulation of the runoff-rainfall process in forest lands is essential for forest land management. In this research, a hydrologic modelling system (HEC-HMS) and artificial neural network (ANN) were applied to simulate the rainfall-runoff process (RRP) in forest lands of Kasilian watershed with an area of 68 square kilometres. The HMS model was performed using the secondary data of rainfall and discharge at the climatology and hydrometric stations, the Soil Conservation Service (SCS) for simulating a flow hydrograph, the curve number (CN) method for runoff estimation, and lag time method for flow routing. Further, a multilayer perceptron (MLP) network was used for simulating the rainfall-runoff process. HEC-HMS model was used to optimize the initial loss (IL) values in the rainfall-runoff process as an input. IL reflects the conditions of vegetation, soil infiltration, and antecedent moisture condition (AMC) in soil. Then, IL values and also incremental rainfall were applied as inputs into ANN to simulate the runoff values. The comparison of the results of simulating the RRP in two scenarios, using IL and without IL, showed that the IL parameter has a high effect in increasing the simulation performance of the rainfall-runoff process. Moreover, ANN predictions were more precise in comparison with those of the HMS model. Further, forest lands can significantly increase IL values and decrease runoff generation.

Suggested Citation

  • Vahid Gholami & Mohammad Reza Khaleghi, 2021. "A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 67(4), pages 165-174.
    • Handle: RePEc:caa:jnljfs:v:67:y:2021:i:4:id:90-2020-jfs
    DOI: 10.17221/90/2020-JFS
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    References listed on IDEAS

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    1. Kwan Lee & Wei-Chiao Hung & Chung-Chieh Meng, 2008. "Deterministic Insight into ANN Model Performance for Storm Runoff Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(1), pages 67-82, January.
    2. Mohammad Reza KHALEGHI, 2017. "The influence of deforestation and anthropogenic activities on runoff generation," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 63(6), pages 245-253.
    3. Mohammad Reza KHALEGHI, 2018. "Application of dendroclimatology in evaluation of climatic changes," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 64(3), pages 139-147.
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    Cited by:

    1. Shanhu Jiang & Yongwei Zhu & Liliang Ren & Denghua Yan & Ying Liu & Hao Cui & Menghao Wang & Chong-Yu Xu, 2023. "A Complementary Streamflow Attribution Framework Coupled Climate, Vegetation and Water Withdrawal," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4807-4822, September.

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